Project Summary Cellular aging is a complex biological process, associated with many diseases, such as cancer, diabetes, and neurodegenerative diseases. New therapeutic approaches to slow aging hold promise for reducing global healthcare burdens of chronic diseases. However, the development of these approaches requires a deep understanding of the mechanisms of aging, which remains a challenging goal. Static population-based studies are insufficient to reveal sophisticated molecular mechanisms that underlie the aging process, because genetically identical cells have various intrinsic causes of aging and widely different rates of aging. Furthermore, although many single genes have profound effects on lifespan, how they interact and function within gene regulatory networks to regulate aging and how these interactions change dynamically during aging remain largely unknown. To overcome these challenges, we have developed high-throughput microfluidic technologies to track the dynamics of molecular processes throughout the replicative lifespans of single S.cerevisiae cells. In the proposed research, these dynamic measurement technologies will be integrated with computational modeling to systematically characterize and quantify the collective dynamic behaviors of aging-related molecular networks. In Aim 1, we will quantitatively characterize the phenotypic changes associated with distinct causes of cell aging and, based on these data, construct a phenomenological model of the aging process, upon which we will build mechanistic models of the conserved Sir2 and protein kinase A (PKA)-regulated molecular networks, both of which are deeply connected to aging. In particular, in Aim 2, we will develop a mechanistic model of the Sir2-regulated molecular network to predict its dynamics and regulatory roles during aging. High-throughout single-cell analysis will be performed to track the dynamics of Sir2-regulated genes and test the model predictions. In Aim 3, we will systematically characterize the PKA- regulated stress response during aging and develop a mechanistic model to quantify and predict the effects of environmental cues on aging. We will systematically examine the dynamics and contribution of stress response genes under various environmental perturbations. These experimental measurements will be used to test the predictions, refine the model, and more importantly, provide insight into the basic mechanisms underlying the environmental control of aging. To accomplish these aims, we have assembled a strong interdisciplinary team of investigators with complementary expertise, who will work synergistically to tackle fundamental questions in the biology of aging from a systems biology perspective. !